In the finance industry, roles in fraud detection and risk modeling demand a solid understanding of specific statistical techniques and machine learning models. Candidates should be proficient in causal inference, parametric and non-parametric tests, and various ML models. For these positions, a minimum of 3 years of experience is typically required. Key areas of focus include anomaly detection in time series, where methods such as z-score, ARIMA, SARIMA, and median absolute deviation are commonly used. Understanding these techniques in practice is crucial for preparing for interviews and succeeding in these roles. This knowledge not only aids in detecting fraud but also in managing risk effectively within the financial sector.
Source: www.reddit.com

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